app.py

import numpy as np
from flask import Flask, request, jsonify, render_template
import simpletransformers
import pandas as pd
import requests
from simpletransformers.ner import NERModel
import json 
import io
from flask_csv import send_csv
from flask import make_response

app = Flask(__name__)


@app.route('/')
def home():
    return render_template('index.html')

@app.route('/predict',methods=['POST',"GET"])
def predict():
    '''
    For rendering results on HTML GUI
    '''
    int_features = [x for x in request.form.values()]
    print(int_features)

    sentence = int_features[0]
    
    model1 = NERModel('bert', 'NERMODEL1',
                  labels=["B-sector","I-sector","B-funda","O","operator","threshold","Join","B-attr","I-funda","TPQty","TPUnit","Sortby", "B-eco","I-eco","B-index","Capitalization","I-","funda","B-security",'I-security','Number','Sector','TPMonth','TPYr','TPRef'],
                  args={"save_eval_checkpoints": False,
      "save_steps": -1,
      "output_dir": "NERMODEL",
      'overwrite_output_dir': True,
      "save_model_every_epoch": False,
      'reprocess_input_data': True, 
      "train_batch_size": 10,'num_train_epochs': 15,"max_seq_length": 64}, use_cuda=False)

    predictions, raw_outputs = model1.predict([sentence])


    if int_features[1] == 'display':
     
        result = json.dumps(predictions[0])
        
        return render_template('index.html', prediction_text=result)

    elif int_features[1] == 'getcsv':
        l=[]
        print(predictions[0])
        print(predictions[0][0])
        print(type(predictions[0][0]))
        for i in predictions[0]:
            dic={}
            for j in i.keys():
                dic['word']=j
                dic['tag']=i[j]
            l.append(dic)
        print(l)
        return send_csv(l,"tags.csv",["word","tag"])

@app.route('/predict_api',methods=['POST'])
def predict_api():
    '''
    For direct API calls trought request
    '''
    data = request.get_json(force=True)

    sentence=data['sentence']

    model1 = NERModel('bert', 'NERMODEL1',
                  labels=["B-sector","I-sector","B-funda","O","operator","threshold","Join","B-attr","I-funda","TPQty","TPUnit","Sortby", "B-eco","I-eco","B-index","Capitalization","I-","funda","B-security",'I-security','Number','Sector','TPMonth','TPYr','TPRef'],
                  args={"save_eval_checkpoints": False,
      "save_steps": -1,
      "output_dir": "NERMODEL",
      'overwrite_output_dir': True,
      "save_model_every_epoch": False,
      'reprocess_input_data': True, 
      "train_batch_size": 10,'num_train_epochs': 15,"max_seq_length": 64}, use_cuda=False)

    predictions, raw_outputs = model1.predict([sentence])
    
    output = predictions

    print(jsonify(output))

    return jsonify(output)


if __name__ == "__main__":
    app.run(debug=True)